Opus Mt Tc Big En Hu

🚀 opus-mt-tc-big-en-hu

A neural machine translation model designed to translate from English (en) to Hungarian (hu).

This model is an integral part of the OPUS-MT project. The project aims to make neural machine translation models widely available and accessible for numerous languages worldwide. All models are initially trained using the remarkable Marian NMT framework, an efficient NMT implementation written purely in C++. These models are then converted to pyTorch using the transformers library by Hugging Face. The training data is sourced from OPUS, and the training pipelines follow the procedures of OPUS-MT-train.

• Publications: OPUS-MT – Building open translation services for the World and The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT (Please cite these publications if you use this model.)

@inproceedings{tiedemann-thottingal-2020-opus,
    title = "{OPUS}-{MT} {--} Building open translation services for the World",
    author = {Tiedemann, J{\"o}rg  and Thottingal, Santhosh},
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://aclanthology.org/2020.eamt-1.61",
    pages = "479--480",
}

@inproceedings{tiedemann-2020-tatoeba,
    title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
    author = {Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.wmt-1.139",
    pages = "1174--1182",
}

✨ Features

• Multilingual Support: Supports translation from English to Hungarian.
• Part of OPUS-MT Project: Benefits from the resources and community of the OPUS-MT project.
• Converted to PyTorch: Compatible with the transformers library for easy integration.

📦 Installation

No explicit installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "I wish I hadn't seen such a horrible film.",
    "She's at school."
]

model_name = "pytorch-models/opus-mt-tc-big-en-hu"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))

for t in translated:
    print( tokenizer.decode(t, skip_special_tokens=True) )

# expected output:
#     Bárcsak ne láttam volna ilyen szörnyű filmet.
#     Iskolában van.

Advanced Usage

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-hu")
print(pipe("I wish I hadn't seen such a horrible film."))

# expected output: Bárcsak ne láttam volna ilyen szörnyű filmet.

📚 Documentation

Model info

Benchmarks

• Test set translations: opusTCv20210807+bt_transformer-big_2022-02-25.test.txt
• Test set scores: opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt
• Benchmark results: benchmark_results.txt
• Benchmark output: benchmark_translations.zip

Acknowledgements

The work is supported by the European Language Grid as pilot project 2866, by the FoTran project, funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the MeMAD project, funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by CSC -- IT Center for Science, Finland.

Model conversion info

📄 License

This model is released under the CC-BY-4.0 license.